A key task of data science is to identify relevant features linked certain output variables that are supposed be modeled or predicted. To obtain a small but meaningful model, it important find stochastically independent capturing all the information necessary model predict sufficiently. Therefore, we introduce in this work framework detect linear and non-linear dependencies between different fe...

We propose a principal components regression method based on maximizing joint pseudo-likelihood for responses and predictors. Our uses both predictors to select linear combinations of the relevant regression, thereby addressing an oft-cited deficiency conventional regression. The proposed estimator is shown be consistent in wide range settings, including ones with non-normal dependent observati...

the generalized principal ideal theorem is one of the cornerstones of dimension theory for noetherian rings. for an r-module m, we identify certain submodules of m that play a role analogous to that of prime ideals in the ring r. using this definition, we extend the generalized principal ideal theorem to modules.

spectral decomposition of time series has a significant role in seismic data processing and interpretation. since the earth acts as a low-pass filter, it changes frequency content of passing seismic waves. conventional representing methods of signals in time domain and frequency domain cannot show time and frequency information simultaneously. time-frequency transforms upgraded spectral decompo...

The inverse problem of finding a matrix with prescribed principal minors is considered. A condition that implies a constructive algorithm for solving this problem will always succeed is presented. The algorithm is based on reconstructing matrices from their principal submatrices and Schur complements in a recursive manner. Consequences regarding the overdeterminancy of this inverse problem are ...

resignation of an attorney has important effects on the principal and the process of trial. if the court or principal do not know about the resignation of the attorney, it may create irreparable losses for principal or cause prolongation of proceedings. so determining exact and comprehensive criterions about the resignation of attorney is necessary. if the attorney does not observe these criter...

Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. This paper compares two methods for dealing with many predictors, that is, principal component regression (PCR) and principal covariate regression (PCovR). The forecast performance of these methods is compared by simulating data from factor models and from regression models. The simulations show that, ...

We consider the problem of outlier rejection in single subspace learning. Classical approaches work with a direct representation of the subspace, and are thus efficient when the subspace dimension is small. Our approach works with a dual representation of the subspace and hence aims to find its orthogonal complement; as such it is particularly suitable for high-dimensional subspaces. We pose th...